Improving Graph Few-shot Learning with Hyperbolic Space and Denoising Diffusion
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918475420663808 |
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| author | Liu, Yonghao Sun, Jialu Pang, Wei Giunchiglia, Fausto Li, Ximing Feng, Xiaoyue Guan, Renchu |
| author_facet | Liu, Yonghao Sun, Jialu Pang, Wei Giunchiglia, Fausto Li, Ximing Feng, Xiaoyue Guan, Renchu |
| contents | Graph few-shot learning, which focuses on effectively learning from only a small number of labeled nodes to quickly adapt to new tasks, has garnered significant research attention. Despite recent advances in graph few-shot learning that have demonstrated promising performance, existing methods still suffer from several key limitations. First, during the meta-training phase, these methods typically perform node representation learning in Euclidean space, which often fails to capture the inherently hierarchical structure existing in real-world graph data. Second, during the meta-testing phase, they usually fit an empirical target distribution derived from only a few support samples, even when this distribution significantly deviates from the true underlying distribution. To address these issues, we propose IMPRESS, a novel framework that IMproves graPh few-shot learning with hypeRbolic spacE and denoiSing diffuSion. Specifically, our model learns node representations in a hyperbolic space and enriches the support distribution through denoising diffusion mechanisms. Theoretically, IMPRESS achieves a tighter generalization bound. Empirically, IMPRESS consistently outperforms competitive baselines across multiple benchmark datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_27462 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Improving Graph Few-shot Learning with Hyperbolic Space and Denoising Diffusion Liu, Yonghao Sun, Jialu Pang, Wei Giunchiglia, Fausto Li, Ximing Feng, Xiaoyue Guan, Renchu Machine Learning Artificial Intelligence Graph few-shot learning, which focuses on effectively learning from only a small number of labeled nodes to quickly adapt to new tasks, has garnered significant research attention. Despite recent advances in graph few-shot learning that have demonstrated promising performance, existing methods still suffer from several key limitations. First, during the meta-training phase, these methods typically perform node representation learning in Euclidean space, which often fails to capture the inherently hierarchical structure existing in real-world graph data. Second, during the meta-testing phase, they usually fit an empirical target distribution derived from only a few support samples, even when this distribution significantly deviates from the true underlying distribution. To address these issues, we propose IMPRESS, a novel framework that IMproves graPh few-shot learning with hypeRbolic spacE and denoiSing diffuSion. Specifically, our model learns node representations in a hyperbolic space and enriches the support distribution through denoising diffusion mechanisms. Theoretically, IMPRESS achieves a tighter generalization bound. Empirically, IMPRESS consistently outperforms competitive baselines across multiple benchmark datasets. |
| title | Improving Graph Few-shot Learning with Hyperbolic Space and Denoising Diffusion |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2604.27462 |